Fed-Batch Feeding Strategies for Enzymatic Biodiesel Production

Price, Jason Anthony; Nordblad, Mathias; Woodley, John M.; Huusom, Jakob Kjøbsted

doi:10.3182/20140824-6-za-1003.01438

Cited by 6 publications

(11 citation statements)

References 19 publications

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“…We followed a methanol feeding strategy similar to the one proposed by Samukawa et al They found that they could increase the reuse of the immobilized enzyme (a clear indication of a reduction in enzyme deactivation), by keeping the methanol content in the reactor below the concentration that gave the highest initial rate of FAME production ( CH critical ) . In our previous work we found CH critical to be 0.525 Eq . We then combined the model of the system (Eq.…”

Section: Resultsmentioning

confidence: 99%

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Mechanistic modeling of biodiesel production using a liquid lipase formulation

Price

Hofmann

Silva

et al. 2014

Biotechnology Progress

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In this article, a kinetic model for the enzymatic transesterification of rapeseed oil with methanol using Callera™ Trans L (a liquid formulation of a modified Thermomyces lanuginosus lipase) was developed from first principles. We base the model formulation on a Ping-Pong Bi-Bi mechanism. Methanol inhibition, along with the interfacial and bulk concentrations of the enzyme was also modeled. The model was developed to describe the effect of different oil compositions, as well as different water, enzyme, and methanol concentrations, which are relevant conditions needed for process evaluation, with respect to the industrial production of biodiesel. The developed kinetic model, coupled with a mass balance of the system, was fitted to and validated on experimental results for the fed-batch transesterification of rapeseed oil. The confidence intervals of the parameter estimates, along with the identifiability of the model parameters were presented. The predictive capability of the model was tested for a case using 0.5% (wt. Enzyme/wt. Oil), 0.5% (wt. Water /wt. Oil) and feeding 1.5 times the stoichiometric amount of methanol in total over 24 h. For this case, an optimized methanol feeding profile that constrains the amount of methanol in the reactor was computed and the predictions experimentally validated. Monte-Carlo simulations were then used to characterize the effect of the parameter uncertainty on the model outputs, giving a biodiesel yield, based on the mass of oil, of 90.8 ± 0.55 mass %.

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Section: Resultsmentioning

confidence: 99%

“…42 In our previous work we found CH critical to be 0.525 Eq. 43 We then combined the model of the system (Eq. 4) with the objective function in Eq.…”

Section: Uncertainty Analysis: Monte-carlo Simulationsmentioning

confidence: 99%

Mechanistic modeling of biodiesel production using a liquid lipase formulation

Price

Hofmann

Silva

et al. 2014

Biotechnology Progress

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“…In TLL-catalyzed methanolysis reaction between crude non-degummed soybean oil and methanol, the yield of fatty acid methyl ester (biodiesel) was over 96% (w/w of oil) in the presence of 3-5% (w/w of oil) water [4]. This indicates that TLL is an efficient, convenient and soluble biocatalyst for cost-effective biodiesel production in water-requiring systems [4], [6] and [8]. Despite its great promise, the use of TLL to catalyze methanolysis reactions has its limitation because the presence of high concentrations of methanol can inactivate the enzyme [4], [5] and [8].…”

Section: Introductionmentioning

confidence: 90%

“…Enzymatic biodiesel production with lipases has attracted broad attention from both academia and industry since it offers an environmentally friendly condition with simplified separation and purification processes compared to the chemical production of biodiesel [1], [2], [3] and [4]. Recently, lipase from ascomycetous fungus Thermomyces lanuginosus (TLL) has been explored for enzymatic biodiesel production due to its high methanolysis activity [5], [6], [7] and [8]. In TLL-catalyzed methanolysis reaction between crude non-degummed soybean oil and methanol, the yield of fatty acid methyl ester (biodiesel) was over 96% (w/w of oil) in the presence of 3-5% (w/w of oil) water [4].…”

Section: Introductionmentioning

confidence: 99%

New insights into the molecular mechanism of methanol-induced inactivation ofThermomyces lanuginosuslipase: a molecular dynamics simulation study

Tong

Busk

Lange

et al. 2015

Molecular Simulation

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Methanol intolerance of lipase is a major limitation in lipase-catalyzed methanolysis reactions.In this study, to understand the molecular mechanism of methanol-induced inactivation of lipases, we performed molecular dynamics (MD) simulations of Thermomyces lanuginosus lipase (TLL) in water and methanol and compared the observed structural and dynamic properties. The solvent accessibility analysis showed that in methanol, polar residues tended to be buried away from the solvent while non-polar residues tended to be more solventexposed in comparison to those in water. Moreover, we observed that in methanol, the van der Waals packing of the core residues in two hydrophobic regions of TLL became weak.

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“…In our previous work, we devised and validated a mechanistic model describing the transesterification of rapeseed oil using a liquid lipase . However, when the model is used for prediction of an optimal methanol feeding profile, the model showed poor prediction of the FFA concentration in the latter half of the reaction compared with the measured FFA value.…”

Section: Introductionmentioning

confidence: 99%

Real‐time model based process monitoring of enzymatic biodiesel production

Price

Nordblad

Woodley

et al. 2014

Biotechnology Progress

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In this contribution we extend our modelling work on the enzymatic production of biodiesel where we demonstrate the application of a Continuous-Discrete Extended Kalman Filter (a state estimator). The state estimator is used to correct for mismatch between the process data and the process model for Fed-batch production of biodiesel. For the three process runs investigated, using a single tuning parameter, qx = 2 × 10(-2) which represents the uncertainty in the process model, it was possible over the entire course of the reaction to reduce the overall mean and standard deviation of the error between the model and the process data for all of the five measured components (triglycerides, diglycerides, monoglycerides, fatty acid methyl esters, and free fatty acid). The most significant reduction for the three process runs, were for the monoglyceride and free fatty acid concentration. For those components, there was over a ten-fold decrease in the overall mean error for the state estimator prediction compared with the predictions from the pure model simulations. It is also shown that the state estimator can be used as a tool for detection of outliers in the measurement data. For the enzymatic biodiesel process, given the infrequent and sometimes uncertain measurements obtained we see the use of the Continuous-Discrete Extended Kalman Filter as a viable tool for real time process monitoring.

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Fed-Batch Feeding Strategies for Enzymatic Biodiesel Production

Cited by 6 publications

References 19 publications

Mechanistic modeling of biodiesel production using a liquid lipase formulation

Mechanistic modeling of biodiesel production using a liquid lipase formulation

New insights into the molecular mechanism of methanol-induced inactivation ofThermomyces lanuginosuslipase: a molecular dynamics simulation study

Real‐time model based process monitoring of enzymatic biodiesel production

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